Inhibition of tumor propellant glutathione peroxidase 4 induces ferroptosis in cancer cells and enhances anticancer effect of cisplatin

Glutathione peroxidase 4 (GPX4) has been confirmed to inhibit ferroptosis in cancer cells, however, whether GPX4 serves as an oncogene is not clear. In this study, the expression of GPX4 and its influence to survival of patients with cancer were analyzed via public databases. Furthermore, the epigenetic regulation of GPX4 and the relation between GPX4 and chemoresistance of different anticancer drugs was also detected. Most importantly, cytological assays were performed to investigate the function of GPX4 in cancer cells. The results showed that GPX4 was higher expressed in cancer tissues than normal and was negatively associated with prognosis of patients. Furthermore, at upstream of GPX4 there was low DNA methylation sites and enhanced level of H3K4me3 and H3K27ac, indicating that high level of GPX4 in cancer may resulted from epigenetic regulation. Moreover, GPX4 was positively related to chemoresistance of anticancer drugs L-685458, lapatinib, palbociclib, and topotecan. In addition, GPX4 may potentially be involved in translation of protein, mitochondrial respiratory chain complex I assembly, electron transport oxidative phosphorylation, nonalcoholic fatty liver disease, and metabolic pathways. Finally, we detected that GPX4 inhibited ferroptosis in cancer cells, the inhibition of GPX4 via RSL3 could enhance the anticancer effect of cisplatin in vitro and in vivo. In conclusion, GPX4 acts as an oncogene and inhibits ferroptosis in cancer cells, the anticancer effect of cisplatin can be enhanced by GPX4 inhibition.     

Stochastic Sensitivity Analysis and Kernel Inference via Distributional Data

Cellular processes are noisy due to the stochastic nature of biochemical reactions. As such, it is impossible to predict the exact quantity of a molecule or other attributes at the single-cell level. However, the distribution of a molecule over a population is often deterministic and is governed by the underlying regulatory networks relevant to the cellular functionality of interest. Recent studies have started to exploit this property to infer network states. To facilitate the analysis of distributional data in a general experimental setting, we introduce a computational framework to efficiently characterize the sensitivity of distributional output to changes in external stimuli. Further, we establish a probability-divergence-based kernel regression model to accurately infer signal level based on distribution measurements. Our methodology is applicable to any biological system subject to stochastic dynamics and can be used to elucidate how population-based information processing may contribute to organism-level functionality. It also lays the foundation for engineering synthetic biological systems that exploit population decoding to more robustly perform various biocomputation tasks, such as disease diagnostics and environmental-pollutant sensing.     

Endocytosis of superoxide dismutase by rat liver.

We have investigated the endocytosis by rat liver of superoxide dismutase (SOD) labelled with 125I. (125I) SOD is quickly taken up by the liver where it remains in significant amounts for at least 150 min. Adsorptive endocytosis is probably involved. Distribution of radioactivity was established after differential and isopycnic centrifugation and compared with that of cathepsin C, a lysosomal enzyme. Results show that the behavior of radioactivity is similar to that of the hydrolase. SOD activity is only marginally affected by incubation in the presence of a purified lysosome extract; moreover, when (125I) SOD is treated in the same conditions, only a few percent of radioactivity becomes acidosoluble. These observations indicate that SOD taken up by the liver accumulates in lysosomes where it can stay for a relatively long time owing to its relative resistance to lysosomal hydrolases.     

Information for targeting to the chloroplastic inner envelope membrane is contained in the mature region of the maize Bt1-encoded protein.

Based on the protein sequence deduced from a cDNA clone, it has been proposed that the maize bt1 locus encodes an amyloplast membrane metabolite translocator protein (Sullivan, T. D., Strelow, L. I., Illingworth, C. A., Phillips, R. L., and Nelson, O. E., Jr. (1991) Plant Cell 3, 1337-1348). The present work provides further evidence for this hypothesis by showing that the gene product of Bt1 could be imported into chloroplasts in vitro and processed to lower molecular weight mature proteins. More importantly, the imported mature proteins were localized to the inner envelope membrane, where metabolite translocators are located in plastids. In addition, the location of information for targeting to the inner membrane was investigated by constructing and analyzing the import of chimeric precursor proteins. A chimeric protein with the transit peptide of the precursor to the small subunit of ribulose-1,5-bisphosphate carboxylase fused to the mature region of the Bt1-encoded protein was targeted to the inner envelope membrane of chloroplasts. Moreover, a chimeric protein with the transit peptide of the Bt1-encoded protein fused to the mature protein of the light-harvesting chlorophyll a/b binding protein was targeted to the thylakoid. These results indicate that the transit peptide of the Bt1-encoded protein functions primarily as a stromal targeting sequence. The information for targeting to the chloroplastic inner envelope membrane is contained in the mature region of the protein.     

Genome‐scale identification of miRNA–mRNA and miRNA–lncRNA interactions in domestic animals

Domestic animals show considerable genetic diversity. Previous studies suggested that animal phenotypes were affected by miRNA–mRNA interplay, but these studies focused mainly on the analysis of one or several miRNA–mRNA interactions. However, in this study, we investigated miRNA–mRNA and miRNA–lncRNA interactions on a genomic scale using miranda and targetscan algorithms. There has been strong directional artificial selection practiced during the domestication of animals. Thus, we investigated SNPs that were located in miRNAs and miRNA binding sites and found that several SNPs located in 3′‐UTRs of mRNAs had the potential to affect miRNA–mRNA interactions. In addition, a database, named miRBond, was developed to provide visualization, analysis and downloading of the resulting datasets. Our results open the way to further experimental verification of miRNA–mRNA and miRNA–lncRNA interactions as well as the influence of SNPs upon such interplay.